The present invention relates to the art of medical diagnostics. It finds particular application in conjunction with CT blood flow mapping of the brain and will be described with particular reference thereto. However, it is to be appreciated that the present invention will also find utility in conjunction with other imaging modalities, such as digital x-ray, magnetic resonance, radiation and positron emission, ultrasound, and the like. The present invention is further applicable to imaging other regions of human and veterinary patients, inanimate objects, and other subjects.
In the human brain, blood reaches the tissue in two modes, directly through the arteries and indirectly through other tissue. In normal, healthy brain tissue, blood reaches gray matter at 40 to 140 ml per 100 ml per minute. Gray matter tissue which receives less than 30 ml per 100 ml per minute is not adequately fed for proper functioning and may suffer irrepairable damage. In white matter, cerebral blood flows are typically about one third of those for gray matter, with flows under about 10 ml per 100 ml per minute being considered inadequate. The early detection of brain regions with subnormal blood flows enables corrective action to be taken before the blood tissue is irreversibly damaged.
One of the most common causes of insufficient feeding of the tissue is a blockage in the arterial blood flow. In the past, iodine was utilized as an enhancement agent injected into the blood to facilitate the location of arterial blockages. However, brain tissue membrane blocked the iodine enhancement agent from permeating the tissue. Because the iodine was unable to pass from the blood flow into the tissue, iodine was only able to enhance images of blood in arteries, capillaries, and veins. Iodine was unable to enhance representations of the actual profusion of blood into the tissues.
Unlike iodine, xenon passes from the blood into the brain tissue. Thus, utilizing the xenon as an enhancement agent facilitates the imaging and measurement of blood profusion into the tissue. As the concentration of xenon gas in the patient's blood rises, the concentration of xenon gas in the brain tissue increases, asymptotically approaching an equilibrium concentration. The rate of the exponential xenon gas concentration increase in the tissue is indicative of the blood flow rate. The equilibrium concentration which is asymptotically approached is indicative of a partition coefficient, .lambda.. The partition coefficient, which is different for different kinds of tissue, is defined as the ratio of the quantity of xenon in each unit volume or voxel of tissue to the quantity of xenon per like volume in blood. For gray matter, the partition coefficient is typically about 0.95 and for white matter is typically about 1.35. Partition coefficients which differ significantly from these values are indicative of sick or dying tissue.
The xenon concentration in the tissue of the ith unit volume or voxel at a time t is described by a formula known as the Kety equation: ##EQU1## where C is the tissue xenon concentration, C.sub.a is the blood xenon concentration, K is the tissue clearance or build-up rate, and f is the flow rate. The partition coefficient, .lambda., is related to the flow rate and the clearance or build-up rate by the equation: EQU f=.lambda.K (2),
where .lambda. is the tissue-blood partition coefficient.
The blood xenon concentration is readily monitorable. The tissue xenon concentration for a tissue in a given voxel can be calculated from the CT number or other data value of a pixel of a CT image corresponding to the given voxel. By taking several CT images at different times, with the blood xenon concentration known for times preceeding each image, one can theoretically solve the Kety equation to determine the partition coefficient and blood flow for the tissue compartment corresponding to each pixel. Typically, three to six images were taken. More particularly, the values or CT numbers from the corresponding pixels of each of the three to six images were iteratively fit to the "best" flow f and partition coefficient which, with the known C.sub.a (w), allowed comparative C(t) to be calculated using any of various conventional curve fitting techniques. Perhaps the most common approximation implemented was the "minimum chi-square" curve fitting criterion which required extended and time consuming calculations. The chi-square curve fitting technique determined a best fit flow, a best fit partition coefficient, and a fit or confidence value indicative of the closeness of the best fit. The curve fitting technique was repeated for each pixel of the images.
It is to be appreciated that chi-square and other curve fitting techniques for fitting three to six data points with a curve, then deriving the slope, the end point which the curve is asymptotically approaching, and the degree of conformity to the curve or best fit was a time consuming operation. When this operation was repeated over 65,000 times to fit the CT numbers of corresponding pixels of a conventional 256.times.256 image to corresponding curves, the computational time became excessive, even on a high speed computer. To reduce the computation to an acceptable time, the image resolution was commonly reduced from 256.times.256 pixels to as little as 32.times.32. However, calculating the flow, partition coefficient, and fit for each of the over 1000 pixels of a 32.times.32 image still required up to ten minutes.
The present invention contemplates a new and improved technique for more rapidly and more accurately determining the flow, the partition coefficient, and the fit or confidence value from CT or other image data.